Meteorological and Oceanographic Ensemble Forecast Application System (EFAS)
Ensembles of meteorological and oceanographic (METOC) numerical forecast models are an excellent tool for quantifying
the uncertainties in the natural environment that impact real-world operations. One challenge in exposing the information
rich ensemble data to forecast consumers and to existing decision aids (DAs) is to present the information into a look
and feel that the consumer quickly understands and to format it so that it can be put into DAs. Our approach is to use
the ensemble to improve forecast skill and guidance as it applies to the specific operation being supported without exposing
the consumer to the probabilistic information about the ensemble.
In our post processing engine, known as the Ensemble Forecast Application System (EFAS), the METOC ensemble is distilled
into a forecast that has a familiar deterministic look and feel for the consumer, and it fits into their existing forecast
products and DAs.
EFAS first applies a bias-correction to some of the ensemble parameters to improve their forecast skill. For example;
we see that forecasts of temperature, pressure, wave height, and wind speed are improved by applying bias-corrections,
while wind direction forecasts are not. Then by using a consensus finding algorithm based on the RMSE history of the
forecast parameter, it is possible to select the most skillful forecast value or forecast field or member from the ensemble.
We call this the consensus forecast. From the ensemble members, one can also extract a spread, or forecast range, around
that forecast value by forming the probability density function from the ensemble members. The parameter’s probability
density function, based on the operator’s requirement for accuracy or the operator’s tolerance in the forecast error,
can also be used to state the confidence (high or low) that the forecast will meet the operator’s needs as it pertains
to the operation.
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